diff --git a/docs/indexes/postgres/Dockerfile b/docs/indexes/postgres/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..25c756e1af59c3df24056811422a79fdda765c68
--- /dev/null
+++ b/docs/indexes/postgres/Dockerfile
@@ -0,0 +1,13 @@
+FROM postgres:latest
+
+RUN apt-get update && \
+ apt-get install -y build-essential postgresql-server-dev-all git && \
+ git clone https://github.com/pgvector/pgvector.git && \
+ cd pgvector && \
+ make && \
+ make install && \
+ cd .. && \
+ rm -rf pgvector && \
+ apt-get remove -y build-essential postgresql-server-dev-all git && \
+ apt-get autoremove -y && \
+ apt-get clean
diff --git a/docs/indexes/postgres/postgres.compose.yaml b/docs/indexes/postgres/postgres.compose.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cc0bdb29c537ac9771e68b33da40cb9b6c47829d
--- /dev/null
+++ b/docs/indexes/postgres/postgres.compose.yaml
@@ -0,0 +1,16 @@
+version: '3.8'
+
+services:
+ pgvector:
+ build: .
+ environment:
+ POSTGRES_DB: semantic_router
+ POSTGRES_USER: admin
+ POSTGRES_PASSWORD: root
+ volumes:
+ - db_data:/var/lib/postgresql/data
+ ports:
+ - "5432:5432"
+
+volumes:
+ db_data:
diff --git a/docs/indexes/postgres/postgres.ipynb b/docs/indexes/postgres/postgres.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f0f868d56877e69d2e54e0bb77c6fe2c80d97be2
--- /dev/null
+++ b/docs/indexes/postgres/postgres.ipynb
@@ -0,0 +1,414 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Postgres pgvector Index Example\n",
+ "\n",
+ "**Note**: You'll require Docker to be installed locally, or a remote instance of Postgres with the pgvector extension installed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Start the Postgres instance with the pgvector extension using Docker Compose\n",
+ "!echo \"Running Docker Compose to start Postgres instance with pgvector extension\"\n",
+ "!docker compose -f ./docs/indexes/postgres/postgres.compose.yaml up -d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/vittorio/.cache/pypoetry/virtualenvs/semantic-router-EZimjtOW-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import necessary modules\n",
+ "from semantic_router import Route\n",
+ "\n",
+ "# Define routes to guide the chatbot's responses\n",
+ "politics = Route(\n",
+ " name=\"politics\",\n",
+ " utterances=[\n",
+ " \"isn't politics the best thing ever\",\n",
+ " \"why don't you tell me about your political opinions\",\n",
+ " \"don't you just love the president\",\n",
+ " \"don't you just hate the president\",\n",
+ " \"they're going to destroy this country!\",\n",
+ " \"they will save the country!\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "# Define a chitchat route for general conversations\n",
+ "chitchat = Route(\n",
+ " name=\"chitchat\",\n",
+ " utterances=[\n",
+ " \"how's the weather today?\",\n",
+ " \"how are things going?\",\n",
+ " \"lovely weather today\",\n",
+ " \"the weather is horrendous\",\n",
+ " \"let's go to the chippy\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "# Combine both routes into a single list\n",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import necessary modules\n",
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders import OpenAIEncoder\n",
+ "\n",
+ "# Set OpenAI API key for the encoder\n",
+ "# You can get your API key from platform.openai.com\n",
+ "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\") or getpass(\n",
+ " \"Enter OpenAI API key: \"\n",
+ ")\n",
+ "encoder = OpenAIEncoder()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import the Postgres index module\n",
+ "from semantic_router.index.postgres import PostgresIndex\n",
+ "import os\n",
+ "\n",
+ "# Set Postgres connection string\n",
+ "os.environ[\"POSTGRES_CONNECTION_STRING\"] = (\n",
+ " \"postgresql://admin:root@localhost:5432/semantic_router\"\n",
+ ")\n",
+ "# Initialize the Postgres index\n",
+ "postgres_index = PostgresIndex()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import the RouteLayer class\n",
+ "from semantic_router.layer import RouteLayer\n",
+ "\n",
+ "# Initialize the RouteLayer with the encoder, routes, and index\n",
+ "rl = RouteLayer(encoder=encoder, routes=routes, index=postgres_index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Check Route Layer and Index Information\n",
+ "We can check our route layer and index information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['politics', 'chitchat']"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# List the names of the defined routes\n",
+ "rl.list_route_names()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Check the total number of entries in the index\n",
+ "len(rl.index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## View All Records for a Given Route\n",
+ "We can also view all of the records for a given route:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['politics#af3cd8c8-defb-5940-b66a-d59e8dbbada7',\n",
+ " 'politics#ce06418e-133f-5484-83fb-d8d3f136fc5d',\n",
+ " 'politics#c24f2d13-c2d5-5ddf-9169-03646cc3dad0',\n",
+ " 'politics#5f21f564-3d1e-58d9-a0b5-9e0c79e33d72',\n",
+ " 'politics#f248c2fa-4ab0-5bf9-8678-b25f7945705a']"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Get all records for the 'politics' route\n",
+ "rl.index._get_route_ids(route_name=\"politics\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Query the Routes\n",
+ "We can query the routes to get the appropriate responses."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='politics', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Query the route layer with a statement related to politics\n",
+ "rl(\"I like voting. What do you think about the president?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'chitchat'"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Query the route layer with a chitchat statement\n",
+ "rl(\"how's the weather today?\").name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'chitchat'"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Query the route layer with another chitchat statement\n",
+ "rl(\"where are you?\").name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Delete or Update Routes\n",
+ "We can delete or update routes as needed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Check the total number of entries in the index before deletion\n",
+ "len(rl.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "5"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Delete the 'chitchat' route and check the index length after deletion\n",
+ "import time\n",
+ "\n",
+ "rl.delete(route_name=\"chitchat\")\n",
+ "time.sleep(1)\n",
+ "len(rl.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Attempt to query the deleted 'chitchat' route\n",
+ "print(rl(\"how's the weather today?\").name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('politics', \"isn't politics the best thing ever\"),\n",
+ " ('politics', \"why don't you tell me about your political opinions\"),\n",
+ " ('politics', \"don't you just love the president\"),\n",
+ " ('politics', \"they're going to destroy this country!\"),\n",
+ " ('politics', 'they will save the country!')]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Get all the current routes and their utterances\n",
+ "rl.index.get_routes()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'type': 'postgres', 'dimensions': 1536, 'total_vector_count': 5}"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Describe the index to get details like type, dimensions, and total vector count\n",
+ "rl.index.describe()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "semantic_router_1",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index f5c72edba5b133645b88d377880111013ba26a11..fb2e049abad2d3357aea7af734a7eb3daa3f1e0e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,91 +1,103 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.3.5"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "aiohappyeyeballs-2.3.5-py3-none-any.whl", hash = "sha256:4d6dea59215537dbc746e93e779caea8178c866856a721c9c660d7a5a7b8be03"},
+ {file = "aiohappyeyeballs-2.3.5.tar.gz", hash = "sha256:6fa48b9f1317254f122a07a131a86b71ca6946ca989ce6326fff54a99a920105"},
+]
+
[[package]]
name = "aiohttp"
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+version = "3.10.1"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
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optional = false
python-versions = "<4.0,>=3.8"
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arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
dev = ["pre-commit", "ruff"]
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dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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name = "google-cloud-aiplatform"
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[package.dependencies]
-grpcio = ">=1.62.2"
+grpcio = ">=1.62.3"
protobuf = ">=4.21.6,<5.0dev"
setuptools = "*"
@@ -1743,13 +1788,13 @@ files = [
[[package]]
name = "huggingface-hub"
-version = "0.22.2"
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description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
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@@ -4813,18 +5026,18 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
[[package]]
name = "transformers"
-version = "4.40.2"
+version = "4.44.0"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = true
python-versions = ">=3.8.0"
files = [
- {file = "transformers-4.40.2-py3-none-any.whl", hash = "sha256:71cb94301ec211a2e1d4b8c8d18dcfaa902dfa00a089dceca167a8aa265d6f2d"},
- {file = "transformers-4.40.2.tar.gz", hash = "sha256:657b6054a2097671398d976ad46e60836e7e15f9ea9551631a96e33cb9240649"},
+ {file = "transformers-4.44.0-py3-none-any.whl", hash = "sha256:ea0ff72def71e9f4812d9414d4803b22681b1617aa6f511bd51cfff2b44a6fca"},
+ {file = "transformers-4.44.0.tar.gz", hash = "sha256:75699495e30b7635ca444d8d372e138c687ab51a875b387e33f1fb759c37f196"},
]
[package.dependencies]
filelock = "*"
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+huggingface-hub = ">=0.23.2,<1.0"
numpy = ">=1.17"
packaging = ">=20.0"
pyyaml = ">=5.1"
@@ -4837,17 +5050,16 @@ tqdm = ">=4.27"
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accelerate = ["accelerate (>=0.21.0)"]
agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
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audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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codecarbon = ["codecarbon (==1.2.0)"]
deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
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flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
ftfy = ["ftfy"]
integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
@@ -4857,25 +5069,26 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
ray = ["ray[tune] (>=2.7.0)"]
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+ruff = ["ruff (==0.5.1)"]
sagemaker = ["sagemaker (>=2.31.0)"]
sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
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-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
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tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-timm = ["timm"]
+timm = ["timm (<=0.9.16)"]
tokenizers = ["tokenizers (>=0.19,<0.20)"]
torch = ["accelerate (>=0.21.0)", "torch"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"]
@@ -4904,13 +5117,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
[[package]]
name = "types-pyyaml"
-version = "6.0.12.20240311"
+version = "6.0.12.20240808"
description = "Typing stubs for PyYAML"
optional = false
python-versions = ">=3.8"
files = [
- {file = "types-PyYAML-6.0.12.20240311.tar.gz", hash = "sha256:a9e0f0f88dc835739b0c1ca51ee90d04ca2a897a71af79de9aec5f38cb0a5342"},
- {file = "types_PyYAML-6.0.12.20240311-py3-none-any.whl", hash = "sha256:b845b06a1c7e54b8e5b4c683043de0d9caf205e7434b3edc678ff2411979b8f6"},
+ {file = "types-PyYAML-6.0.12.20240808.tar.gz", hash = "sha256:b8f76ddbd7f65440a8bda5526a9607e4c7a322dc2f8e1a8c405644f9a6f4b9af"},
+ {file = "types_PyYAML-6.0.12.20240808-py3-none-any.whl", hash = "sha256:deda34c5c655265fc517b546c902aa6eed2ef8d3e921e4765fe606fe2afe8d35"},
]
[[package]]
@@ -4929,13 +5142,13 @@ types-urllib3 = "*"
[[package]]
name = "types-requests"
-version = "2.32.0.20240523"
+version = "2.32.0.20240712"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
- {file = "types-requests-2.32.0.20240523.tar.gz", hash = "sha256:26b8a6de32d9f561192b9942b41c0ab2d8010df5677ca8aa146289d11d505f57"},
- {file = "types_requests-2.32.0.20240523-py3-none-any.whl", hash = "sha256:f19ed0e2daa74302069bbbbf9e82902854ffa780bc790742a810a9aaa52f65ec"},
+ {file = "types-requests-2.32.0.20240712.tar.gz", hash = "sha256:90c079ff05e549f6bf50e02e910210b98b8ff1ebdd18e19c873cd237737c1358"},
+ {file = "types_requests-2.32.0.20240712-py3-none-any.whl", hash = "sha256:f754283e152c752e46e70942fa2a146b5bc70393522257bb85bd1ef7e019dcc3"},
]
[package.dependencies]
@@ -4954,24 +5167,24 @@ files = [
[[package]]
name = "typing-extensions"
-version = "4.12.0"
+version = "4.12.2"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
- {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"},
- {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
+ {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+ {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
]
[[package]]
name = "urllib3"
-version = "1.26.18"
+version = "1.26.19"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
files = [
- {file = "urllib3-1.26.18-py2.py3-none-any.whl", hash = "sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07"},
- {file = "urllib3-1.26.18.tar.gz", hash = "sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0"},
+ {file = "urllib3-1.26.19-py2.py3-none-any.whl", hash = "sha256:37a0344459b199fce0e80b0d3569837ec6b6937435c5244e7fd73fa6006830f3"},
+ {file = "urllib3-1.26.19.tar.gz", hash = "sha256:3e3d753a8618b86d7de333b4223005f68720bcd6a7d2bcb9fbd2229ec7c1e429"},
]
[package.extras]
@@ -4981,13 +5194,13 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
[[package]]
name = "urllib3"
-version = "2.2.1"
+version = "2.2.2"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
files = [
- {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
- {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
+ {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+ {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
]
[package.extras]
@@ -5136,18 +5349,18 @@ multidict = ">=4.0"
[[package]]
name = "zipp"
-version = "3.19.0"
+version = "3.19.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
- {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
- {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
+ {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+ {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
]
[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[extras]
bedrock = ["boto3", "botocore"]
@@ -5157,6 +5370,7 @@ hybrid = ["pinecone-text"]
local = ["llama-cpp-python", "tokenizers", "torch", "transformers"]
mistralai = ["mistralai"]
pinecone = ["pinecone-client"]
+postgres = []
processing = ["matplotlib"]
qdrant = ["qdrant-client"]
vision = ["pillow", "torch", "torchvision", "transformers"]
@@ -5164,4 +5378,4 @@ vision = ["pillow", "torch", "torchvision", "transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "c9728a3449a45fafec8fa8220005ab5ed81e6b5162560d86e6ff627b955d9f68"
+content-hash = "c33563710453ed3b10e2ca2c7715a504a0727273fb5f4b19181630d711d2e60d"
diff --git a/pyproject.toml b/pyproject.toml
index 8b743aa4ed7e423392c430ff0ed801c9a454d425..5969754b72093fe95be4f99e555399be5bbd3383 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -44,6 +44,7 @@ boto3 = { version = "^1.34.98", optional = true }
botocore = {version = "^1.34.110", optional = true}
aiohttp = "^3.9.5"
fastembed = {version = "^0.3.0", optional = true}
+psycopg2 = "^2.9.9"
[tool.poetry.extras]
hybrid = ["pinecone-text"]
@@ -55,6 +56,7 @@ mistralai = ["mistralai"]
qdrant = ["qdrant-client"]
google = ["google-cloud-aiplatform"]
bedrock = ["boto3", "botocore"]
+postgres = ["psycopg"]
fastembed = ["fastembed"]
[tool.poetry.group.dev.dependencies]
diff --git a/semantic_router/index/postgres.py b/semantic_router/index/postgres.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c971d4d1a71ea51a05f0698b4f7aa971aeeef5d
--- /dev/null
+++ b/semantic_router/index/postgres.py
@@ -0,0 +1,482 @@
+import os
+import uuid
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import psycopg2
+from pydantic import BaseModel
+
+from semantic_router.index.base import BaseIndex
+from semantic_router.schema import Metric
+
+
+class MetricPgVecOperatorMap(Enum):
+ """
+ Enum to map the metric to PostgreSQL vector operators.
+ """
+
+ cosine = "<=>"
+ dotproduct = "<#>" # inner product
+ euclidean = "<->" # L2 distance
+ manhattan = "<+>" # L1 distance
+
+
+def parse_vector(vector_str: Union[str, Any]) -> List[float]:
+ """
+ Parses a vector from a string or other representation.
+
+ :param vector_str: The string or object representation of a vector.
+ :type vector_str: Union[str, Any]
+ :return: A list of floats representing the vector.
+ :rtype: List[float]
+ """
+ if isinstance(vector_str, str):
+ vector_str = vector_str.strip('()"[]')
+ return list(map(float, vector_str.split(",")))
+ else:
+ return vector_str
+
+
+def clean_route_name(route_name: str) -> str:
+ """
+ Cleans and formats the route name by stripping spaces and replacing them with hyphens.
+
+ :param route_name: The original route name.
+ :type route_name: str
+ :return: The cleaned and formatted route name.
+ :rtype: str
+ """
+ return route_name.strip().replace(" ", "-")
+
+
+class PostgresIndexRecord(BaseModel):
+ """
+ Model to represent a record in the Postgres index.
+ """
+
+ id: str = ""
+ route: str
+ utterance: str
+ vector: List[float]
+
+ def __init__(self, **data) -> None:
+ """
+ Initializes a new Postgres index record with given data.
+
+ :param data: Field values for the record.
+ :type data: dict
+ """
+ super().__init__(**data)
+ clean_route = self.route.strip().replace(" ", "-")
+ if len(clean_route) > 255:
+ raise ValueError(
+ f"The cleaned route name '{clean_route}' exceeds the 255 character limit."
+ )
+ route_namespace_uuid = uuid.uuid5(uuid.NAMESPACE_DNS, clean_route)
+ hashed_uuid = uuid.uuid5(route_namespace_uuid, self.utterance)
+ self.id = clean_route + "#" + str(hashed_uuid)
+
+ def to_dict(self) -> Dict:
+ """
+ Converts the record to a dictionary.
+
+ :return: A dictionary representation of the record.
+ :rtype: Dict
+ """
+ return {
+ "id": self.id,
+ "vector": self.vector,
+ "route": self.route,
+ "utterance": self.utterance,
+ }
+
+
+class PostgresIndex(BaseIndex):
+ """
+ Postgres implementation of Index.
+ """
+
+ connection_string: Optional[str] = None
+ index_prefix: str = "semantic_router_"
+ index_name: str = "index"
+ dimensions: int = 1536
+ metric: Metric = Metric.COSINE
+ namespace: Optional[str] = ""
+ conn: Optional[psycopg2.extensions.connection] = None
+ type: str = "postgres"
+
+ def __init__(
+ self,
+ connection_string: Optional[str] = None,
+ index_prefix: str = "semantic_router_",
+ index_name: str = "index",
+ dimensions: int = 1536,
+ metric: Metric = Metric.COSINE,
+ namespace: Optional[str] = "",
+ ):
+ """
+ Initializes the Postgres index with the specified parameters.
+
+ :param connection_string: The connection string for the PostgreSQL database.
+ :type connection_string: Optional[str]
+ :param index_prefix: The prefix for the index table name.
+ :type index_prefix: str
+ :param index_name: The name of the index table.
+ :type index_name: str
+ :param dimensions: The number of dimensions for the vectors.
+ :type dimensions: int
+ :param metric: The metric used for vector comparisons.
+ :type metric: Metric
+ :param namespace: An optional namespace for the index.
+ :type namespace: Optional[str]
+ """
+ super().__init__()
+ if connection_string:
+ self.connection_string = connection_string
+ else:
+ connection_string = os.environ.get("POSTGRES_CONNECTION_STRING")
+ if not connection_string:
+ raise ValueError("No connection string provided")
+ self.connection_string = connection_string
+ self.index_prefix = index_prefix
+ self.index_name = index_name
+ self.dimensions = dimensions
+ self.metric = metric
+ self.namespace = namespace
+ self.conn = psycopg2.connect(dsn=self.connection_string)
+ self.setup_index()
+
+ def _get_table_name(self) -> str:
+ """
+ Returns the name of the table for the index.
+
+ :return: The table name.
+ :rtype: str
+ """
+ return f"{self.index_prefix}{self.index_name}"
+
+ def _get_metric_operator(self) -> str:
+ """
+ Returns the PostgreSQL operator for the specified metric.
+
+ :return: The PostgreSQL operator.
+ :rtype: str
+ """
+ return MetricPgVecOperatorMap[self.metric.value].value
+
+ def _get_score_query(self, embeddings_str: str) -> str:
+ """
+ Creates the select statement required to return the embeddings distance.
+
+ :param embeddings_str: The string representation of the embeddings.
+ :type embeddings_str: str
+ :return: The SQL query part for scoring.
+ :rtype: str
+ """
+ operator = self._get_metric_operator()
+ if self.metric == Metric.COSINE:
+ return f"1 - (vector {operator} {embeddings_str}) AS score"
+ elif self.metric == Metric.DOTPRODUCT:
+ return f"(vector {operator} {embeddings_str}) * -1 AS score"
+ elif self.metric == Metric.EUCLIDEAN:
+ return f"vector {operator} {embeddings_str} AS score"
+ elif self.metric == Metric.MANHATTAN:
+ return f"vector {operator} {embeddings_str} AS score"
+ else:
+ raise ValueError(f"Unsupported metric: {self.metric}")
+
+ def setup_index(self) -> None:
+ """
+ Sets up the index by creating the table and vector extension if they do not exist.
+
+ :raises ValueError: If the existing table's vector dimensions do not match the expected dimensions.
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ if not self._check_embeddings_dimensions():
+ raise ValueError(
+ f"The length of the vector embeddings in the existing table {table_name} does not match the expected dimensions of {self.dimensions}."
+ )
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ cur.execute(
+ f"""
+ CREATE EXTENSION IF NOT EXISTS vector;
+ CREATE TABLE IF NOT EXISTS {table_name} (
+ id VARCHAR(255) PRIMARY KEY,
+ route VARCHAR(255),
+ utterance TEXT,
+ vector VECTOR({self.dimensions})
+ );
+ COMMENT ON COLUMN {table_name}.vector IS '{self.dimensions}';
+ """
+ )
+ self.conn.commit()
+
+ def _check_embeddings_dimensions(self) -> bool:
+ """
+ Checks if the length of the vector embeddings in the table matches the expected dimensions, or if no table exists.
+
+ :return: True if the dimensions match or the table does not exist, False otherwise.
+ :rtype: bool
+ :raises ValueError: If the vector column comment does not contain a valid integer.
+ """
+ table_name = self._get_table_name()
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ cur.execute(
+ f"SELECT EXISTS(SELECT 1 FROM information_schema.tables WHERE table_name='{table_name}');"
+ )
+ fetch_result = cur.fetchone()
+ exists = fetch_result[0] if fetch_result else None
+ if not exists:
+ return True
+ cur.execute(
+ f"""SELECT col_description('{table_name}'::regclass, attnum) AS column_comment
+ FROM pg_attribute
+ WHERE attrelid = '{table_name}'::regclass
+ AND attname='vector'"""
+ )
+ result = cur.fetchone()
+ dimension_comment = result[0] if result else None
+ if dimension_comment:
+ try:
+ vector_length = int(dimension_comment.split()[-1])
+ return vector_length == self.dimensions
+ except ValueError:
+ raise ValueError(
+ "The 'vector' column comment does not contain a valid integer."
+ )
+ else:
+ raise ValueError("No comment found for the 'vector' column.")
+
+ def add(
+ self, embeddings: List[List[float]], routes: List[str], utterances: List[Any]
+ ) -> None:
+ """
+ Adds vectors to the index.
+
+ :param embeddings: A list of vector embeddings to add.
+ :type embeddings: List[List[float]]
+ :param routes: A list of route names corresponding to the embeddings.
+ :type routes: List[str]
+ :param utterances: A list of utterances corresponding to the embeddings.
+ :type utterances: List[Any]
+ :raises ValueError: If the vector embeddings being added do not match the expected dimensions.
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ new_embeddings_length = len(embeddings[0])
+ if new_embeddings_length != self.dimensions:
+ raise ValueError(
+ f"The vector embeddings being added are of length {new_embeddings_length}, which does not match the expected dimensions of {self.dimensions}."
+ )
+ records = [
+ PostgresIndexRecord(vector=vector, route=route, utterance=utterance)
+ for vector, route, utterance in zip(embeddings, routes, utterances)
+ ]
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ cur.executemany(
+ f"INSERT INTO {table_name} (id, route, utterance, vector) VALUES (%s, %s, %s, %s) ON CONFLICT (id) DO NOTHING", # if matching hash exists do nothing.
+ [
+ (record.id, record.route, record.utterance, record.vector)
+ for record in records
+ ],
+ )
+ self.conn.commit()
+
+ def delete(self, route_name: str) -> None:
+ """
+ Deletes records with the specified route name.
+
+ :param route_name: The name of the route to delete records for.
+ :type route_name: str
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ cur.execute(f"DELETE FROM {table_name} WHERE route = '{route_name}'")
+ self.conn.commit()
+
+ def describe(self) -> Dict:
+ """
+ Describes the index by returning its type, dimensions, and total vector count.
+
+ :return: A dictionary containing the index's type, dimensions, and total vector count.
+ :rtype: Dict
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ cur.execute(f"SELECT COUNT(*) FROM {table_name}")
+ count = cur.fetchone()
+ if count is None:
+ count = 0
+ else:
+ count = count[0] # Extract the actual count from the tuple
+ return {
+ "type": self.type,
+ "dimensions": self.dimensions,
+ "total_vector_count": count,
+ }
+
+ def query(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ ) -> Tuple[np.ndarray, List[str]]:
+ """
+ Searches the index for the query vector and returns the top_k results.
+
+ :param vector: The query vector.
+ :type vector: np.ndarray
+ :param top_k: The number of top results to return.
+ :type top_k: int
+ :param route_filter: Optional list of routes to filter the results by.
+ :type route_filter: Optional[List[str]]
+ :return: A tuple containing the scores and routes of the top_k results.
+ :rtype: Tuple[np.ndarray, List[str]]
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ filter_query = f" AND route = ANY({route_filter})" if route_filter else ""
+ # Create the string representation of vector
+ vector_str = f"'[{','.join(map(str, vector.tolist()))}]'"
+ score_query = self._get_score_query(vector_str)
+ operator = self._get_metric_operator()
+ cur.execute(
+ f"SELECT route, {score_query} FROM {table_name} WHERE true{filter_query} ORDER BY vector {operator} {vector_str} LIMIT {top_k}"
+ )
+ results = cur.fetchall()
+ return np.array([result[1] for result in results]), [
+ result[0] for result in results
+ ]
+
+ def _get_route_ids(self, route_name: str):
+ """
+ Retrieves all vector IDs for a specific route.
+
+ :param route_name: The name of the route to retrieve IDs for.
+ :type route_name: str
+ :return: A list of vector IDs.
+ :rtype: List[str]
+ """
+ clean_route = clean_route_name(route_name)
+ ids, _ = self._get_all(route_name=f"{clean_route}")
+ return ids
+
+ def _get_all(
+ self, route_name: Optional[str] = None, include_metadata: bool = False
+ ):
+ """
+ Retrieves all vector IDs and optionally metadata from the Postgres index.
+
+ :param route_name: Optional route name to filter the results by.
+ :type route_name: Optional[str]
+ :param include_metadata: Whether to include metadata in the results.
+ :type include_metadata: bool
+ :return: A tuple containing the list of vector IDs and optionally metadata.
+ :rtype: Tuple[List[str], List[Dict]]
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+
+ query = "SELECT id"
+ if include_metadata:
+ query += ", route, utterance"
+ query += f" FROM {table_name}"
+
+ if route_name:
+ query += f" WHERE route LIKE '{route_name}%'"
+
+ all_vector_ids = []
+ metadata = []
+
+ with self.conn.cursor() as cur:
+ cur.execute(query)
+ results = cur.fetchall()
+ for row in results:
+ all_vector_ids.append(row[0])
+ if include_metadata:
+ metadata.append({"sr_route": row[1], "sr_utterance": row[2]})
+
+ return all_vector_ids, metadata
+
+ def get_routes(self) -> List[Tuple]:
+ """
+ Gets a list of route and utterance objects currently stored in the index.
+
+ :return: A list of (route_name, utterance) tuples.
+ :rtype: List[Tuple]
+ """
+ # Get all records with metadata
+ _, metadata = self._get_all(include_metadata=True)
+ # Create a list of (route_name, utterance) tuples
+ route_tuples = [(x["sr_route"], x["sr_utterance"]) for x in metadata]
+ return route_tuples
+
+ def delete_all(self):
+ """
+ Deletes all records from the Postgres index.
+
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ cur.execute(f"DELETE FROM {table_name}")
+ self.conn.commit()
+
+ def delete_index(self) -> None:
+ """
+ Deletes the entire table for the index.
+
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ cur.execute(f"DROP TABLE IF EXISTS {table_name}")
+ self.conn.commit()
+
+ def __len__(self):
+ """
+ Returns the total number of vectors in the index.
+
+ :return: The total number of vectors.
+ :rtype: int
+ :raises TypeError: If the database connection is not established.
+ """
+ table_name = self._get_table_name()
+ if not isinstance(self.conn, psycopg2.extensions.connection):
+ raise TypeError("Index has not established a connection to Postgres")
+ with self.conn.cursor() as cur:
+ cur.execute(f"SELECT COUNT(*) FROM {table_name}")
+ count = cur.fetchone()
+ if count is None:
+ return 0
+ return count[0]
+
+ class Config:
+ """
+ Configuration for the Pydantic BaseModel.
+ """
+
+ arbitrary_types_allowed = True